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Nicklas Vinter

Development and validation of prediction models for incident atrial fibrillation in heart failure

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  • Nicklas Vinter
  • Thomas Alexander Gerds, University of Copenhagen
  • ,
  • Pia Cordsen, Aalborg University
  • ,
  • Jan Brink Valentin, Aalborg University
  • ,
  • Gregory Y H Lip, University of Liverpool, Aalborg University
  • ,
  • Emelia J J Benjamin, Boston University
  • ,
  • Søren Paaske Johnsen, Aalborg University
  • ,
  • Lars Frost

OBJECTIVES: Accurate prediction of heart failure (HF) patients at high risk of atrial fibrillation (AF) represents a potentially valuable tool to inform shared decision making. No validated prediction model for AF in HF is currently available. The objective was to develop clinical prediction models for 1-year risk of AF.

METHODS: Using the Danish Heart Failure Registry, we conducted a nationwide registry-based cohort study of all incident HF patients diagnosed from 2008 to 2018 and without history of AF. Administrative data sources provided the predictors. We used a cause-specific Cox regression model framework to predict 1-year risk of AF. Internal validity was examined using temporal validation.

RESULTS: The population included 27 947 HF patients (mean age 69 years; 34% female). Clinical experts preselected sex, age at HF, NewYork Heart Association (NYHA) class, hypertension, diabetes mellitus, chronic kidney disease, obstructive sleep apnoea, chronic obstructive pulmonary disease and myocardial infarction. Among patients aged 70 years at HF, the predicted 1-year risk was 9.3% (95% CI 7.1% to 11.8%) for males and 6.4% (95% CI 4.9% to 8.3%) for females given all risk factors and NYHA III/IV, and 7.5% (95% CI 6.7% to 8.4%) and 5.1% (95% CI 4.5% to 5.8%), respectively, given absence of risk factors and NYHA class I. The area under the curve was 65.7% (95% CI 63.9% to 67.5%) and Brier score 7.0% (95% CI 5.2% to 8.9%).

CONCLUSION: We developed a prediction model for the 1-year risk of AF. Application of the model in routine clinical settings is necessary to determine the possibility of predicting AF risk among patients with HF more accurately and if so, to quantify the clinical effects of implementing the model in practice.

Original languageEnglish
Article number002169
JournalOpen Heart
Number of pages9
Publication statusPublished - Jan 2023

    Research areas

  • Atrial Fibrillation, Electronic Health Records, HEART FAILURE

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